We apply state-of-the-art models for natural language entailment [1]}, [2]} to solve the text span-pair relation extraction task as a sentence pair classification problem.
While these models show promising results on the span-pair RE dataset we generated, they do not fully exploit all the information of our dataset. For example, in our dataset, a text span with context information is the combination of matched text span and its surrounding context sentences (Fig.REF ). But in the normal sentence pair classification setting, they are concatenated into a single sequence while its segmentation is ignored. Here, we explored some variants of the neural model, to incorporate the context segmentation and position information with the state-of-the-art model for sentence pair classification.
